HIGH PERFORMANCE BIOMEDICAL COMPUTING

高性能生物医学计算

• 批准号: 6103832
• 负责人: Edward Suh
• 金额: --
• 依托单位: CENTER FOR INFORMATION TECHNOLOGY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/6103832
• 关键词: computer assisted sequence analysis; computer center; computer data analysis; computer network; computer program /software; computer simulation; computer system design /evaluation; computer system hardware; digital imaging; electron microscopy; image processing; linkage mapping; molecular dynamics; parallel processing; protein folding; quantum chemistry; radiation therapy dosage; supercomputer

The goals of the High Performance Biomedical Computing Program are to identify and solve those computational problems that can benefit from high-performance computing and communications hardware, modern software engineering principles, and efficient algorithms. This effort includes providing high-performacne parallel computer systems for the NIH and development of parallel algorithms for biomedical applications.Using a high-performance parallel computer, biomedical scientists can greatly reduce the time it takes to complete computationally intensive tasks and adopt new approaches for processing experimental data. This may allow for the inclusion of more data in a calculation, the determination of a more accurate result, a reduction in the time needed to complete a long calculation, or the implementation of a new algorithm or more realistic model. With high-bandwidth network connections and interactive user interfaces, parallel computing is readily accessible to a biomedical researcher in the laboratory or clinic at the investigator's computer workstation.In addressing these computational challenges, the Computational Bioscience and Engineering Laboratory (CBEL) is developing algorithms for a wide range of biomedical applications where computational speed is important. These include image processing of electron micrographs, medical imaging, radiation treatment planning, electron paramagnetic resonance imaging and spectroscopy, human genetic linkage analysis, protein folding prediction, nuclear magnetic resonance spectroscopy, x-ray crystallography, quantum chemical method, and molecular dynamics simulations. The ultimate goal is to have high-performance, parallel computing facilitate the science that is done at the NIH. While developing these computationally demanding applications, CBEL is investigating the following high-performance computing issues: partitioning a problem into many parts that can be independently executed on different processors; designing the parts so that the computing load can be distributed evenly over the available processors or dynamically balanced; designing algorithms so that the number of processors is a parameter and the algorithms can be configured dynamically for the available machine; developing tools and environments for producing portable parallel programs; monitoring system performance; and proving that a parallel algorithm on a given machine meets its specifications.

高性能生物医学的目标 计算程序是识别和解决那些计算 可以从高性能计算中受益的问题， 通信硬件，现代软件工程原理， 高效的算法。这项工作包括提供 为NIH和 生物医学并行算法的发展 应用。使用高性能并行计算机， 生物医学科学家可以大大缩短 完成计算密集型任务，并采用新的 处理实验数据的方法。这可以允许 在计算中包含更多的数据， 结果更准确，完成所需时间减少 长计算，或执行一个新的算法或更多 现实模型通过高带宽网络连接， 交互式用户界面，并行计算是很容易访问的 实验室或诊所的生物医学研究人员， 调查员的电脑工作站。在处理这些问题时， 计算挑战，计算生物科学和 工程实验室（CBEL）正在开发算法 广泛的生物医学应用，其中计算速度 重要.这些包括电子显微照片的图像处理， 医学影像，放射治疗计划，电子 顺磁共振成像和光谱学，人类遗传 连锁分析，蛋白质折叠预测，核磁共振 共振光谱学，x射线晶体学，量子化学 方法和分子动力学模拟。最终目标是 拥有高性能的并行计算， 是在国家卫生研究院完成的。在通过计算开发这些 要求苛刻的应用，CBEL正在研究以下内容 高性能计算问题：将问题划分为 许多部分可以独立地在不同的 处理器;设计部件，以便计算负载可以 均匀地分布在可用的处理器上或者动态地 平衡;设计算法，使处理器的数量是一个 参数和算法可以动态配置 可用的机器;开发生产工具和环境 可移植并行程序;监控系统性能;以及 证明给定机器上的并行算法满足其 规范.